Myocardial strain imaging is increasingly used for the assessment of cardiac function ([1-4]). Cine Displacement Encoding with Stimulated Echoes (DENSE) magnetic resonance imaging (MRI), ([5, 6]) is an established myocardial strain imaging technique with high accuracy and rapid data analysis. However, current DENSE protocols require breath-holding, which limits its use in patients who cannot hold their breath, such as pediatric patients and heart failure patients. DENSE MRI uses segmented data acquisition where data are acquired over multiple heartbeats and respiratory motion among the acquisitions of different segments induces artifacts such as blurriness in the images ([7]).
For DENSE imaging in particular, suppression of the artifact-generating echo due to T1 relaxation is vital, which, with breath-holding, is generally achieved by subtraction of complementary phase-cycled datasets ([5, 8]). However, with free-breathing, cancellation of this echo is compromised when phase-cycled datasets are acquired at different respiratory positions. Poor suppression of the T1 relaxation echo due to respiration can result in severe striping artifacts in images. Therefore, breath-holding (BH) is conventionally used to manage these artifacts. BH is rapid and efficient, but not always feasible, especially for many heart failure patients and pediatric patients.
Previously, a diaphragm navigator (dNAV) method has been applied to enable free-breathing (FB) cine DENSE acquisitions ([9]) but with greatly decreased efficiency (˜30%) and variable image quality ([9, 10]). Additionally, the conventional dNAV method requires extra setup of the dNAVs. Another approach is to extract respiratory motion information from the imaging data itself, termed self-navigation. Self-navigation is advantageous compared to dNAV because it extracts motion from acquired image data and often directly estimates motion of the heart due to respiration. Self-navigation can also be combined with rigid motion correction in the k-space domain to improve efficiency. One self-navigation method enables reconstruction of lower-resolution intermediate images (iNAVs) and estimation of respiratory motion using image registration ([11-15]). This strategy is especially suitable for non-Cartesian sampling trajectories that have greater sampling densities near the center of k-space.
In addition to self-navigation, there are other strategies that can enhance the performance of free-breathing imaging. Localized signal generation ([16]) can reduce the field of view (FOV) and therefore reduce motion artifacts generated from unwanted tissues. ([17]) With a smaller FOV, the motion of heart can be represented with a simpler model such as an affine motion.
It is with respect to these and other considerations that the various aspects of the disclosed technology as described below are presented.